{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"sglang","slug":"sglang","name":"SGLang","type":"framework","url":"https://github.com/sgl-project/sglang","page_url":"https://unfragile.ai/sglang","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"sglang__cap_0","uri":"capability://memory.knowledge.radixattention.prefix.caching.with.token.to.kv.mapping","name":"radixattention prefix caching with token-to-kv mapping","description":"Implements a radix-tree based prefix cache that deduplicates and reuses KV cache across requests with shared prefixes, using a token-to-KV mapping system that tracks which tokens map to which cached KV states. The system automatically identifies common prefixes across concurrent requests and avoids redundant computation by serving cached KV pairs, reducing memory bandwidth and compute for subsequent tokens in the same prefix context.","intents":["Reduce KV cache memory footprint when serving multiple requests with overlapping prompts or system messages","Accelerate batch inference when many requests share common context prefixes","Minimize redundant attention computation across requests with identical prompt prefixes"],"best_for":["Teams running high-throughput inference servers with batch requests sharing common prompts","Applications with templated system messages or few-shot examples repeated across requests","Deployments targeting latency-sensitive workloads where KV cache memory is a bottleneck"],"limitations":["Prefix matching requires exact token-level alignment; semantic similarity does not trigger cache hits","Radix tree traversal adds ~5-10ms overhead per request for prefix lookup and validation","Cache invalidation complexity increases with model updates or tokenizer changes","Benefits diminish for workloads with highly diverse prompts or single-request serving patterns"],"requires":["CUDA-capable GPU with sufficient VRAM for KV cache storage","Tokenizer consistency across all requests (same tokenizer instance or compatible versions)","Batch size >= 2 to amortize prefix matching overhead"],"input_types":["text prompts","token sequences"],"output_types":["cached KV tensors","token-to-KV mappings"],"categories":["memory-knowledge","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_1","uri":"capability://planning.reasoning.compressed.finite.state.machines.for.structured.output.generation","name":"compressed finite state machines for structured output generation","description":"Encodes output constraints (JSON schemas, regex patterns, grammar rules) as compressed finite state machines that guide token sampling during generation. The FSM is compiled from constraint specifications and integrated into the sampling pipeline, restricting logits to only tokens that maintain valid state transitions, ensuring generated output conforms to the schema without post-hoc validation or rejection sampling.","intents":["Generate guaranteed-valid JSON or structured output matching a provided schema","Enforce regex or grammar constraints during decoding without rejection sampling overhead","Reduce token waste by eliminating invalid outputs that would require regeneration"],"best_for":["Applications requiring deterministic structured outputs (API responses, database records, form filling)","Teams building agents that parse model outputs into typed data structures","Workloads where output validation is critical and regeneration is expensive"],"limitations":["FSM compilation adds 50-200ms latency per unique constraint specification","Complex nested schemas or deeply recursive grammars produce large FSM state spaces","Constraint violations during sampling are silently corrected by forcing valid transitions, potentially altering semantic intent","Limited support for context-dependent constraints (e.g., conditional fields based on earlier tokens)"],"requires":["Constraint specification in supported format (JSON Schema, EBNF grammar, or regex)","Tokenizer vocabulary must be pre-analyzed to build FSM state transitions","Python 3.9+ for constraint compilation"],"input_types":["JSON Schema","EBNF grammar","regex patterns","constraint specifications"],"output_types":["token sequences conforming to schema","structured JSON","validated text matching grammar"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_10","uri":"capability://tool.use.integration.grpc.server.interface.with.streaming.and.batching","name":"grpc server interface with streaming and batching","description":"Exposes a gRPC server interface for high-performance client-server communication with support for streaming requests/responses and automatic request batching. The gRPC interface handles serialization, connection pooling, and multiplexing of concurrent requests, with lower latency and higher throughput than HTTP for high-frequency clients.","intents":["Serve high-frequency clients (e.g., real-time applications) with lower latency than HTTP","Stream long responses without buffering entire output in memory","Batch multiple client requests transparently for improved GPU utilization"],"best_for":["Real-time applications requiring sub-100ms latency","High-frequency clients where HTTP overhead is significant","Deployments with many concurrent connections (100+)"],"limitations":["gRPC requires protobuf schema definition and code generation","Client libraries are less mature than HTTP libraries for some languages","Debugging gRPC traffic is harder than HTTP (requires specialized tools)","Firewall configuration may be more complex for gRPC (uses HTTP/2)"],"requires":["gRPC server implementation (built-in to SGLang)","gRPC client library for target language","Protobuf schema definition","Python 3.9+"],"input_types":["gRPC messages (protobuf format)","streaming requests"],"output_types":["gRPC messages (protobuf format)","streaming responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_11","uri":"capability://automation.workflow.distributed.inference.with.multi.node.deployment.and.load.balancing","name":"distributed inference with multi-node deployment and load balancing","description":"Orchestrates inference across multiple nodes using tensor parallelism, pipeline parallelism, and data parallelism with automatic load balancing. The system manages inter-node communication via NCCL or gRPC, distributes requests across nodes based on load, and handles node failures with graceful degradation. Supports both synchronous (all-reduce) and asynchronous (pipeline) execution patterns.","intents":["Scale inference to very large models (100B+) across multiple nodes","Distribute load across nodes to maximize throughput and minimize latency","Achieve fault tolerance by distributing computation across redundant nodes"],"best_for":["Large-scale deployments (10+ GPUs across multiple nodes)","Organizations with high-availability requirements","Workloads requiring model sizes that exceed single-node capacity"],"limitations":["Inter-node communication latency (10-100ms) is much higher than intra-node; impacts prefill performance","Load balancing complexity increases with node count; imbalanced loads reduce efficiency","Fault tolerance requires checkpointing and recovery logic; adds complexity and overhead","Network bandwidth becomes bottleneck for large models; requires high-bandwidth interconnect (100Gbps+)"],"requires":["Multiple nodes with CUDA-capable GPUs","High-bandwidth network (100Gbps+ recommended; 10Gbps minimum)","NCCL library for collective communication","Distributed training framework (PyTorch Distributed, Megatron-LM, etc.)","Cluster management (Kubernetes, SLURM, or manual configuration)"],"input_types":["model architecture","node configuration","request batches"],"output_types":["distributed model outputs","load metrics","latency measurements"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_12","uri":"capability://planning.reasoning.sampling.and.output.generation.with.logits.processing.pipeline","name":"sampling and output generation with logits processing pipeline","description":"Implements a configurable sampling pipeline that processes logits through multiple stages: temperature scaling, top-k/top-p filtering, repetition penalties, length penalties, and custom constraints. Each stage is modular and can be enabled/disabled independently, with support for batch-level and token-level parameter variations. The pipeline integrates with the FSM-based constraint system for guaranteed valid outputs.","intents":["Control output diversity and quality through temperature, top-k, and top-p parameters","Prevent repetition and length explosion through penalties","Enforce output constraints (JSON, regex) while maintaining sampling diversity"],"best_for":["Applications requiring fine-grained control over output generation","Workloads combining sampling with hard constraints (structured output)","Deployments where output quality and diversity are critical"],"limitations":["Complex penalty combinations can interact unpredictably; tuning requires experimentation","Logits processing adds 1-5ms per token depending on pipeline complexity","Batch-level parameter variations require separate sampling passes; reduces batching efficiency","Some penalty combinations (e.g., extreme length penalties) can cause mode collapse"],"requires":["Model with logits output","Sampling parameters (temperature, top-k, top-p, penalties)","Python 3.9+"],"input_types":["logits tensors","sampling parameters","constraint specifications"],"output_types":["sampled token indices","token probabilities","constraint-valid tokens"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_13","uri":"capability://automation.workflow.request.scheduling.with.prefill.decode.disaggregation","name":"request scheduling with prefill-decode disaggregation","description":"Implements a scheduler that separates prefill (processing prompt tokens) and decode (generating output tokens) into distinct phases, allowing different batch sizes and scheduling strategies for each. The scheduler batches prefill requests together, then schedules decode operations with higher priority to minimize latency. Supports continuous batching where new requests can be added to the decode queue without waiting for current requests to complete.","intents":["Minimize time-to-first-token by prioritizing prefill operations","Maximize throughput by batching decode operations with higher batch sizes","Support continuous batching where new requests arrive during generation"],"best_for":["Interactive applications where time-to-first-token is critical","High-throughput batch serving where decode throughput matters more than latency","Workloads with variable request arrival patterns"],"limitations":["Prefill-decode disaggregation adds scheduling complexity and overhead","Separate batches for prefill and decode reduce GPU utilization compared to unified batching","Continuous batching requires careful synchronization to avoid race conditions","Optimal batch sizes for prefill and decode differ; tuning is required per workload"],"requires":["Scheduler implementation (built-in to SGLang)","Request queue and priority management","Python 3.9+"],"input_types":["requests with prompts","scheduling parameters (batch sizes, priorities)"],"output_types":["scheduled batches","latency metrics","throughput measurements"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_14","uri":"capability://automation.workflow.model.configuration.and.loading.with.architecture.detection","name":"model configuration and loading with architecture detection","description":"Provides a ModelConfig system that automatically detects model architecture (Llama, Qwen, DeepSeek, etc.) from HuggingFace model cards or manual specification, loads model weights with support for multiple formats (PyTorch, SafeTensors, GGUF), and handles architecture-specific optimizations. The system validates configuration compatibility and provides helpful error messages for unsupported models.","intents":["Load models from HuggingFace without manual architecture specification","Support multiple model formats (PyTorch, SafeTensors, GGUF) transparently","Apply architecture-specific optimizations automatically"],"best_for":["Teams deploying diverse models without deep knowledge of each architecture","Workflows requiring rapid model switching","Deployments supporting multiple model formats"],"limitations":["Architecture detection from model cards is heuristic-based; may fail for custom models","Unsupported architectures require manual ModelConfig definition","Model loading time scales with model size; large models (100B+) take 5-30 minutes","Weight format conversion (e.g., PyTorch to SafeTensors) adds overhead"],"requires":["Model in HuggingFace format or manual ModelConfig","Sufficient disk space for model weights","GPU with sufficient VRAM for model loading","Python 3.9+"],"input_types":["model name (HuggingFace ID)","ModelConfig specification","model weights in supported format"],"output_types":["loaded model","ModelConfig","architecture metadata"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_15","uri":"capability://tool.use.integration.python.engine.api.for.programmatic.inference.without.http.grpc","name":"python engine api for programmatic inference without http/grpc","description":"Provides a Python API for direct programmatic access to the SGLang inference engine, allowing applications to call the model without HTTP or gRPC overhead. The API exposes core functions like `generate()` and `chat()` that accept prompts and return generated text, with full control over generation parameters and access to internal state. This enables embedding SGLang directly in Python applications without network communication.","intents":["Integrate SGLang inference directly into Python applications without network overhead","Access internal model state and intermediate representations for research/debugging","Build Python-based agent systems with direct model access"],"best_for":["Python applications requiring low-latency local inference","Research and development where direct model access is needed","Single-machine deployments where network communication is unnecessary"],"limitations":["Python-only; not suitable for polyglot environments","No built-in request queuing or concurrency control; applications must manage threading","Direct memory access means model crashes can crash the entire Python process"],"requires":["Python 3.8+","SGLang installed as Python package","GPU with CUDA support","Model weights accessible locally"],"input_types":["text prompts","generation parameters (temperature, max_tokens, etc.)"],"output_types":["generated text","token IDs","logits (optional)"],"categories":["tool-use-integration","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_2","uri":"capability://automation.workflow.automatic.parallelism.with.tensor.pipeline.and.expert.parallelism","name":"automatic parallelism with tensor, pipeline, and expert parallelism","description":"Automatically selects and orchestrates tensor parallelism (splitting model weights across GPUs), pipeline parallelism (splitting layers across GPUs), and expert parallelism (distributing MoE experts) based on model size, GPU count, and memory constraints. The system analyzes the model architecture, computes optimal partition strategies, and manages inter-GPU communication and synchronization transparently.","intents":["Deploy large models (70B+) that don't fit on a single GPU without manual parallelism configuration","Maximize throughput by automatically balancing compute and communication across available GPUs","Support MoE models like DeepSeek-V3 with expert-level parallelism without manual routing logic"],"best_for":["Teams deploying models larger than single-GPU VRAM capacity","Multi-GPU clusters (8-256 GPUs) where manual parallelism tuning is impractical","Organizations running MoE models requiring expert parallelism and load balancing"],"limitations":["Automatic strategy selection may not match hand-tuned configurations for specific hardware topologies","Inter-GPU communication (AllReduce, AllGather) adds 10-30% latency overhead depending on network bandwidth","Pipeline parallelism introduces bubble overhead during prefill phase; optimal for decode-heavy workloads","Expert parallelism requires balanced token distribution; skewed routing can cause GPU underutilization"],"requires":["Multiple CUDA-capable GPUs (minimum 2, optimal 8+)","High-bandwidth interconnect (NVLink preferred; PCIe 4.0+ acceptable)","NCCL library for collective communication","Model architecture support (Llama, Qwen, DeepSeek, etc.)"],"input_types":["model architecture definition","hardware configuration","batch specifications"],"output_types":["parallelism strategy configuration","distributed tensor operations","aggregated model outputs"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_3","uri":"capability://automation.workflow.cuda.graph.compilation.with.dynamic.batching","name":"cuda graph compilation with dynamic batching","description":"Pre-compiles model forward passes into CUDA graphs that capture GPU kernel launches and memory operations, then replays these graphs for each batch with dynamic shape handling. The system builds separate graphs for prefill and decode phases, caches graphs based on batch size and sequence length patterns, and reuses them across requests to eliminate CPU-GPU synchronization overhead and kernel launch latency.","intents":["Reduce per-request latency by eliminating CPU overhead from repeated kernel launches","Maximize GPU utilization by batching requests with varying sequence lengths into pre-compiled graphs","Achieve consistent sub-millisecond latency for decode operations through graph replay"],"best_for":["Low-latency serving scenarios where per-token latency matters (chat, real-time applications)","High-throughput batch inference where amortizing graph compilation overhead is critical","Deployments targeting consistent latency SLOs with minimal variance"],"limitations":["CUDA graph compilation adds 100-500ms overhead per unique batch size / sequence length combination","Dynamic shapes require graph recompilation; highly variable request patterns reduce cache hit rates","Graph memory footprint scales with number of cached graphs; large deployments may hit GPU memory limits","Debugging and profiling become harder with compiled graphs; kernel-level visibility is reduced"],"requires":["NVIDIA GPU with CUDA Compute Capability 7.0+ (Volta or newer)","CUDA 11.0+ with graph capture support","Deterministic model execution (no dynamic control flow)"],"input_types":["model forward pass definition","batch configurations","sequence length patterns"],"output_types":["compiled CUDA graphs","model outputs","latency measurements"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_4","uri":"capability://memory.knowledge.multi.tier.kv.cache.storage.with.hicache.and.storage.backends","name":"multi-tier kv cache storage with hicache and storage backends","description":"Implements a hierarchical KV cache storage system (HiCache) that automatically tiers KV data across GPU VRAM, CPU RAM, and optional NVMe storage based on access patterns and memory pressure. The system monitors cache hit rates, predicts which KV states will be accessed, and proactively migrates data between tiers to minimize transfer latency while maximizing effective cache capacity.","intents":["Extend effective KV cache capacity beyond GPU VRAM by spilling to CPU RAM and NVMe","Serve longer sequences or larger batches without OOM errors by intelligent cache tiering","Reduce memory costs by storing infrequently-accessed KV states in slower but cheaper storage"],"best_for":["Long-context inference (4K-100K tokens) where KV cache exceeds GPU VRAM","Cost-sensitive deployments where CPU RAM and NVMe are cheaper than GPU memory","Workloads with predictable access patterns where prefetching can hide transfer latency"],"limitations":["CPU-to-GPU transfers add 5-50ms latency per KV retrieval depending on transfer size and bandwidth","NVMe transfers add 50-500ms latency; only practical for prefill phase or very long sequences","Tiering overhead (migration decisions, prefetch logic) adds 2-5% CPU overhead","Effectiveness depends on access pattern predictability; random access patterns negate benefits"],"requires":["GPU with sufficient VRAM for at least one layer's KV cache","CPU RAM >= 2x GPU VRAM for effective CPU-tier caching","Optional: NVMe SSD with PCIe 4.0+ for storage backend (requires explicit configuration)","Linux kernel with mmap support for efficient CPU-NVMe transfers"],"input_types":["KV cache tensors","access patterns","memory pressure signals"],"output_types":["tiered KV cache","cache migration decisions","performance metrics"],"categories":["memory-knowledge","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_5","uri":"capability://planning.reasoning.speculative.decoding.with.eagle.draft.model.integration","name":"speculative decoding with eagle draft model integration","description":"Implements speculative decoding using EAGLE (a smaller draft model) that predicts multiple future tokens in parallel, which are then verified against the main model in a single forward pass. If verification succeeds, multiple tokens are accepted; if it fails, the draft is rejected and generation continues from the main model. The system integrates EAGLE predictions directly into the scheduling pipeline to minimize verification overhead.","intents":["Accelerate token generation by 1.5-3x by predicting and verifying multiple tokens per forward pass","Reduce main model inference cost by offloading draft generation to a smaller model","Maintain output quality identical to non-speculative generation while improving throughput"],"best_for":["Latency-sensitive applications where token generation speed is critical","Deployments with sufficient GPU memory to run both main and draft models","Workloads where draft model accuracy is high (>70% token acceptance rate)"],"limitations":["Requires training or obtaining a compatible EAGLE draft model for each main model","Draft model must fit in GPU memory alongside main model; adds 10-30% memory overhead","Verification overhead (batch verification of draft tokens) can exceed draft generation savings if acceptance rate is low","Speculative decoding provides no benefit for prefill phase; only accelerates decode","Draft model quality directly impacts acceptance rate; poor drafts reduce speedup to <1.2x"],"requires":["EAGLE draft model compatible with target main model","GPU with sufficient VRAM for both main and draft models (typically 2x main model VRAM)","Supported model architecture (Llama, Qwen, etc.)","Python 3.9+"],"input_types":["main model","EAGLE draft model","prompt tokens"],"output_types":["generated token sequences","acceptance rate metrics","latency measurements"],"categories":["planning-reasoning","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_6","uri":"capability://image.visual.multi.modal.vision.language.model.serving.with.image.preprocessing","name":"multi-modal vision-language model serving with image preprocessing","description":"Handles vision-language models (LLaVA, Qwen-VL, etc.) by preprocessing images into visual tokens, merging them with text tokens, and managing the combined sequence through the model. The system supports multiple image formats (JPEG, PNG, base64), resizes and patches images according to model requirements, and handles variable-length image sequences within batches.","intents":["Serve vision-language models that accept both text and image inputs in a single request","Process batches containing requests with different numbers of images without padding waste","Support image URLs, base64-encoded images, and local file paths transparently"],"best_for":["Applications combining image analysis with text generation (visual QA, image captioning, document understanding)","Teams building multi-modal agents that reason over images and text","Deployments requiring efficient batching of mixed text-image requests"],"limitations":["Image preprocessing (resizing, patching, encoding) adds 50-200ms per image depending on resolution","Variable image counts across batch requests require padding or dynamic batching; reduces GPU utilization","Vision encoder outputs are not cached across requests; each unique image requires re-encoding","Large images (>2K resolution) can exceed token limits; automatic downsampling may lose detail"],"requires":["Vision-language model with supported architecture (LLaVA, Qwen-VL, LLaMA-ViT, etc.)","Image processing library (PIL, OpenCV) for preprocessing","GPU with sufficient VRAM for vision encoder + language model","Python 3.9+"],"input_types":["text prompts","images (JPEG, PNG, base64, URLs)","image metadata (resolution, format)"],"output_types":["text responses","visual tokens","image embeddings"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_7","uri":"capability://code.generation.editing.lora.adapter.loading.and.switching.with.dynamic.model.patching","name":"lora adapter loading and switching with dynamic model patching","description":"Loads and applies LoRA (Low-Rank Adaptation) adapters to model weights at runtime without reloading the base model. The system maintains a registry of available adapters, patches model layers with adapter weights during forward passes, and supports switching between adapters across requests in the same batch. Adapters are merged into base weights for inference efficiency.","intents":["Fine-tune models for specific tasks without storing multiple full model copies","Switch between task-specific adapters (e.g., summarization vs. translation) per-request","Reduce memory overhead of multi-task serving by sharing base model weights across adapters"],"best_for":["Multi-tenant deployments where different customers need task-specific model variants","Fine-tuning workflows where maintaining multiple full models is prohibitive","Applications requiring rapid adapter switching without model reloading"],"limitations":["LoRA adapter loading and weight merging adds 10-50ms per adapter switch","Adapter effectiveness depends on rank and training quality; poorly-trained adapters degrade output","Batching requests with different adapters requires per-request adapter switching; reduces batch efficiency","LoRA is limited to linear layers; non-linear layers cannot be adapted"],"requires":["Base model compatible with LoRA (most transformer models supported)","LoRA adapter weights in compatible format (HuggingFace, PEFT, or SGLang format)","GPU with sufficient VRAM for base model + adapter weights","Python 3.9+"],"input_types":["base model","LoRA adapter weights","adapter configuration (rank, target layers)"],"output_types":["patched model weights","model outputs with adapter applied"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_8","uri":"capability://data.processing.analysis.quantization.with.fp8.fp4.int8.and.modelopt.support","name":"quantization with fp8, fp4, int8, and modelopt support","description":"Supports multiple quantization schemes (FP8, FP4, INT8, MXFP4) with per-layer or per-channel quantization strategies. The system includes a quantization registry that maps quantization types to kernel implementations, handles quantization-aware training integration, and provides fallback kernels for unsupported hardware. Quantized models run with minimal accuracy loss while reducing memory footprint and increasing throughput.","intents":["Reduce model memory footprint by 4-8x through quantization, enabling larger models on same hardware","Accelerate inference by 1.5-3x using quantized matrix multiplications","Deploy models on memory-constrained hardware (mobile, edge) by quantizing to INT8 or FP4"],"best_for":["Cost-sensitive deployments where reducing GPU memory is critical","Throughput-focused workloads where quantization speedup outweighs accuracy loss","Edge deployments with strict memory budgets"],"limitations":["Quantization introduces 0.5-2% accuracy loss depending on scheme and model size","FP8 and FP4 quantization require specialized GPU support (H100, L40S); fallback kernels are slower","Quantization-aware training requires retraining; post-training quantization may degrade quality","Quantized models are not compatible with unquantized LoRA adapters"],"requires":["Model in supported format (HuggingFace, GGUF, or SGLang format)","GPU with quantization kernel support (NVIDIA H100, L40S, or A100 for FP8)","Quantization configuration specifying scheme and target layers","Python 3.9+"],"input_types":["model weights","quantization configuration (scheme, scale, zero-point)","calibration data (optional, for post-training quantization)"],"output_types":["quantized model weights","quantization parameters (scales, zero-points)","model outputs"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__cap_9","uri":"capability://tool.use.integration.openai.compatible.http.api.with.chat.templates.and.conversation.formatting","name":"openai-compatible http api with chat templates and conversation formatting","description":"Exposes an HTTP server with OpenAI API compatibility (chat completions, embeddings endpoints) that automatically formats conversations using model-specific chat templates. The system handles multi-turn conversations, system messages, and tool/function calling through standard OpenAI request/response formats, with automatic template selection based on model type.","intents":["Drop-in replacement for OpenAI API for local or self-hosted deployments","Serve models through standard API without client-side template formatting","Support multi-turn conversations with automatic message formatting"],"best_for":["Teams migrating from OpenAI to self-hosted models without code changes","Applications requiring OpenAI API compatibility for vendor flexibility","Deployments where standardized API contracts are critical"],"limitations":["Not all OpenAI API features are supported (e.g., vision endpoints, function calling for all models)","Chat template selection is automatic; custom templates require configuration","Response streaming adds latency overhead compared to batch responses","Rate limiting and authentication are not built-in; require reverse proxy or middleware"],"requires":["Model with supported chat template (Llama, Qwen, Mistral, etc.)","Python 3.9+","HTTP server (FastAPI, Flask, or built-in SGLang server)","Optional: reverse proxy for authentication and rate limiting"],"input_types":["JSON (OpenAI chat completion request format)","text prompts","conversation history"],"output_types":["JSON (OpenAI chat completion response format)","text completions","streaming responses"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sglang__headline","uri":"capability://deployment.infra.high.performance.framework.for.serving.large.language.and.vision.models","name":"high-performance framework for serving large language and vision models","description":"SGLang is a fast-serving framework designed for large language and vision models, featuring advanced techniques like RadixAttention for efficient prefix caching and automatic parallelism, making it ideal for high-demand AI applications.","intents":["best framework for serving language models","high-performance model serving for AI","framework for vision model deployment","fast serving solution for large models","AI model serving with prefix caching"],"best_for":["large-scale AI applications","real-time model inference"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["CUDA-capable GPU with sufficient VRAM for KV cache storage","Tokenizer consistency across all requests (same tokenizer instance or compatible versions)","Batch size >= 2 to amortize prefix matching overhead","Constraint specification in supported format (JSON Schema, EBNF grammar, or regex)","Tokenizer vocabulary must be pre-analyzed to build FSM state transitions","Python 3.9+ for constraint compilation","gRPC server implementation (built-in to SGLang)","gRPC client library for target language","Protobuf schema definition","Python 3.9+"],"failure_modes":["Prefix matching requires exact token-level alignment; semantic similarity does not trigger cache hits","Radix tree traversal adds ~5-10ms overhead per request for prefix lookup and validation","Cache invalidation complexity increases with model updates or tokenizer changes","Benefits diminish for workloads with highly diverse prompts or single-request serving patterns","FSM compilation adds 50-200ms latency per unique constraint specification","Complex nested schemas or deeply recursive grammars produce large FSM state spaces","Constraint violations during sampling are silently corrected by forcing valid transitions, potentially altering semantic intent","Limited support for context-dependent constraints (e.g., conditional fields based on earlier tokens)","gRPC requires protobuf schema definition and code generation","Client libraries are less mature than HTTP libraries for some languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sglang","compare_url":"https://unfragile.ai/compare?artifact=sglang"}},"signature":"sFsGG6bzvQDb9VZ5opfVYj7ALGwjOC3EQH1E/hF8q6+/ALdq5f2DtwvJZTzom1marPCveB1PpVvWi5Z3ly04DA==","signedAt":"2026-06-21T04:28:08.668Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sglang","artifact":"https://unfragile.ai/sglang","verify":"https://unfragile.ai/api/v1/verify?slug=sglang","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}